An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome.
- Authors
- Ng, Bernard; White, Charles C; Klein, Hans-Ulrich; Sieberts, Solveig K; McCabe, Cristin; Patrick, Ellis; Xu, Jishu; Yu, Lei; Gaiteri, Chris; Bennett, David A; Mostafavi, Sara; De Jager, Philip L
- Year
- 2017
- Journal
- Nature neuroscience
- PMID
- 28869584
- DOI
- 10.1038/nn.4632
- PMCID
- PMC5785926
We report a multi-omic resource generated by applying quantitative trait locus (xQTL) analyses to RNA sequence, DNA methylation and histone acetylation data from the dorsolateral prefrontal cortex of 411 older adults who have all three data types. We identify SNPs significantly associated with gene expression, DNA methylation and histone modification levels. Many of these SNPs influence multiple molecular features, and we demonstrate that SNP effects on RNA expression are fully mediated by epigenetic features in 9% of these loci. Further, we illustrate the utility of our new resource, xQTL Serve, by using it to prioritize the cell type(s) most affected by an xQTL. We also reanalyze published genome wide association studies using an xQTL-weighted analysis approach and identify 18 new schizophrenia and 2 new bipolar susceptibility variants, which is more than double the number of loci that can be discovered with a larger blood-based expression eQTL resource.
Overview of xQTL analysis(A) Graphical summary of our data and analyses. We first associate genetic variation with each data type separately to establish our xQTL reference. We then use these xQTLs to assess whether a given SNP influences more than one data type, whether epigenomic features mediate the effects of SNPs on gene expression, and whether our xQTLs can be leveraged to discover new susceptibility loci. (B) βlog10 p-value of Spearmanβs correlation between SNPs and DNA methylation (mQTL), histone acetylation (haQTL), and gene expression (eQTL) vs. the SNPsβ physical positions in the genome. Each dot represents the strongest association within a cis window for each SNP. (C) Zoomed in Manhattan plot of chromosome 18 to illustrate p-value distribution of xQTLs at a higher resolution.
Cross-tissue replication analysis(A) Scatter plot of βlog10 p-values of associations between the lead brain eQTL SNPs and their associated genes in brain and blood. The dashed red lines denote the significance threshold (Ξ±FWER=0.05 with Bonferroni correction). (B) βlog10 p-value distribution of eQTLs that appear to be brain-specific (light and dark pink dots, the latter are specific to NLRP1). (C) Distribution of p-values from the DGN study restricted to brain eQTLs. Estimated replication rate (Ο1 statistics) between blood and brain eQTLs is 0.83. (D) eQTL p-values at NLRP1 locus. Each dot represents one SNP tested in either brain (ROSMAP) or blood (DGN). The x-axis corresponds to the distance between each assessed cis SNP and NLRP1βs TSS, and the y-axis corresponds to βlog10 p-values for association between SNPs and NLRP1 expression. The LD between the lead SNP in blood and brain is r2 < 0.1.
Genomic enrichment of xQTLs and their overlap(A) Log odds ratio of xQTL SNP enrichment in 15 different chromatin states31 as defined by the Roadmap Epigenomics project via applying ChromHMM to DLPFC samples from two cognitively non-impaired ROSMAP subjects. The error bars reflect standard deviation. (B) Log odds ratio of xQTL SNP enrichment in exons and introns. The error bars reflect standard deviation. (C) Distribution of distance between each lead mQTL SNP and its nearest TSS. (D) Ο1 statistics for assessing xQTL sharing across the three molecular features. Each cell (i,j) corresponds to the proportion of xQTLs of trait j that share the same xQTL SNPs identified in trait i.
Epigenetic mediation of eQTLs(A) Sharing of SNPs between eQTLs, mQTLs, and haQTLs. 2,305,942 SNPs tested for all molecular phenotypes are considered. (B) Three models relating SNPs (s), epigenetic features (methylation/histone acetylation, m/h) and gene expression (g): i) independent model (IM) where effects of SNPs on epigenetic features and transcripts are unrelated, ii) epigenetic mediation model (EM) where epigenetic features mediate the effects of SNPs on gene expression, and iii) transcription mediation model (TM) where the effects of SNPs on epigenetics is mediated through its effect on gene expression. The causal inference test was used for assessing mediation35. (C) Proportion of shared xQTL SNPs that are consistent with each model. (D) Expression level of IL1RL1 vs. number of minor alleles present for rs13015714, which is a shared xQTL SNP that impacts IL1RL1 expression and nearby DNA methylation and histone acetylation levels. The red line corresponds to the mean. The yellow region corresponds to the 95% confidence interval of the mean. The edges of the blue region correspond to Β±1 standard deviation. The SNP effect disappears after regressing out the effect of the mQTL probes and haQTL peaks associated with rs13015714 from IL1RL1 expression. (E) Association between IL1RL1 expression and the levels of its associated methylation probes and acetylation peaks. Colors indicate the genotype of rs13015714: minor allele homozygotes (yellow), heterozygotes (green), major allele homozygotes (blue).
Application of the xQTL Resource for translational studies(A) Enrichment of xQTL SNPs in published GWAS datasets based on the LDSR model39. Enrichments are with respect to two sets of background SNPs: 1) all genome-wide SNPs and 2) SNPs falling in generic functional sites previously defined by LDSR. The error bars reflect standard deviation. (B) βlog10 p-value of interaction test in quantifying cell-specificity. 46 genes that survived FDR correction at a q-threshold of 0.2 shown. (C) Level of CPVL expression vs. a marker of microglial proportion (CD68 gene). CPVL expression is found to increase with increasing proportion of microglia, particularly among major allele homozygotes (pink dots). (DβE) Zoomed in Manhattan plot around the PCNX (D) and CPEB1 (E) loci, showing the results of the published standard GWAS (bottom panel) and our weighted GWAS (top panel). Each dot is one SNP. The dotted green line is the standard genome-wide significance threshold (p < 5x10β8).
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| Genetic impacts on DNA methylation: research findings and future perspectives. | VillicaΓ±a S et al. | β | 2021 | β |
| Genetic Modifiers of Age at Onset for Parkinson's Disease in Asians: A Genome-Wide Association Study. | Li C et al. | β | 2021 | β |
| Genetic underpinnings of affective temperaments: a pilot GWAS investigation identifies a new genome-wide significant SNP for anxious temperament in ADGRB3 gene. | Gonda X et al. | β | 2021 | β |
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| Genome-wide association study followed by trans-ancestry meta-analysis identify 17 new risk loci for schizophrenia. | Liu J et al. | β | 2021 | β |
| Genome-wide association study identifies 48 common genetic variants associated with handedness. | Cuellar-Partida G et al. | β | 2021 | β |
| Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. | Schwartzentruber J et al. | β | 2021 | β |
| Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. | Yang C et al. | β | 2021 | β |
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| Human-lineage-specific genomic elements are associated with neurodegenerative disease and APOE transcript usage. | Chen Z et al. | β | 2021 | β |
| Identification of a Risk Locus at 7p22.3 for Schizophrenia and Bipolar Disorder in East Asian Populations. | Li W et al. | β | 2021 | β |
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| Mendelian randomization integrating GWAS and mQTL data identified novel pleiotropic DNA methylation loci for neuropathology of Alzheimer's disease. | Liu D et al. | β | 2021 | β |
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| Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. | Kapoor M et al. | β | 2021 | β |
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| Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome. | Powell SK et al. | β | 2021 | β |
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| Proximal and distal effects of genetic susceptibility to multiple sclerosis on the T cell epigenome. | Roostaei T et al. | β | 2021 | β |
| rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues. | Hu Y et al. | β | 2021 | β |
| Sex-differential DNA methylation and associated regulation networks in human brain implicated in the sex-biased risks of psychiatric disorders. | Xia Y et al. | β | 2021 | β |
| Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. | Lagomarsino VN et al. | β | 2021 | β |
| Targeting synaptic plasticity in schizophrenia: insights from genomic studies. | Mould AW et al. | β | 2021 | β |
| The patterns of deleterious mutations during the domestication of soybean. | Kim MS et al. | β | 2021 | β |
| Therapeutic Targeting of Histone Deacetylation to Prevent Alzheimer's Disease. | Chacko S et al. | β | 2021 | β |
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| Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility. | Robinson JW et al. | β | 2021 | β |
| Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders. | Hernandez LM et al. | β | 2021 | β |
| Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease. | Wang M et al. | β | 2021 | β |
| Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney. | Eales JM et al. | β | 2021 | β |
| Utilising multi-large omics data to elucidate biological mechanisms within multiple sclerosis genetic susceptibility loci. | Zhou Y et al. | β | 2021 | β |
| Variations and expression features of CYP2D6 contribute to schizophrenia risk. | Ma L et al. | β | 2021 | β |
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| Genetic effects on planum temporale asymmetry and their limited relevance to neurodevelopmental disorders, intelligence or educational attainment. | Carrion-Castillo A et al. | β | 2020 | β |
| Genetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology. | Yang HS et al. | β | 2020 | β |
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| Genetic variation contributes to gene expression response in ischemic stroke: an eQTL study. | Amini H et al. | β | 2020 | β |
| Genome-Wide DNA Methylation Patterns in Persistent Attention-Deficit/Hyperactivity Disorder and in Association With Impulsive and Callous Traits. | Meijer M et al. | β | 2020 | β |
| Genome-wide meta-analysis identified novel variant associated with hallux valgus in Caucasians. | Arbeeva L et al. | β | 2020 | β |
| Identification of a functional human-unique 351-bp Alu insertion polymorphism associated with major depressive disorder in the 1p31.1 GWAS risk loci. | Liu W et al. | β | 2020 | β |
| Integrating genome-wide association study and expression quantitative trait loci data identifies NEGR1 as a causal risk gene of major depression disorder. | Wang X et al. | β | 2020 | β |
| Integration analysis of methylation quantitative trait loci and GWAS identify three schizophrenia risk variants. | Yu H et al. | β | 2020 | β |
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| Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. | Sieberts SK et al. | β | 2020 | β |
| meQTL and ncRNA functional analyses of 102 GWAS-SNPs associated with depression implicate HACE1 and SHANK2 genes. | Ciuculete DM et al. | β | 2020 | β |
| Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. | Erzurumluoglu AM et al. | β | 2020 | β |
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| Resources for functional genomic studies of health and development in nonhuman primates. | Jasinska AJ | β | 2020 | β |
| rs34331204 regulates TSPAN13 expression and contributes to Alzheimer's disease with sex differences. | Hu Y et al. | β | 2020 | β |
| rs4147929 variant minor allele increases ABCA7 gene expression and ABCA7 shows increased gene expression in Alzheimer's disease patients compared with controls. | Liu G et al. | β | 2020 | β |
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| SZDB2.0: an updated comprehensive resource for schizophrenia research. | Wu Y et al. | β | 2020 | β |
| The applied implications of epigenetics in anxiety, affective and stress-related disorders - A review and synthesis on psychosocial stress, psychotherapy and prevention. | Schiele MA et al. | β | 2020 | β |
| The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function, and mushroom dendritic spine. | Yang Z et al. | β | 2020 | β |
| Tissue specific regulation of transcription in endometrium and association with disease. | Mortlock S et al. | β | 2020 | β |
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| A genomic atlas of systemic interindividual epigenetic variation in humans. | Gunasekara CJ et al. | β | 2019 | β |
| A Role of Oxytocin Receptor Gene Brain Tissue Expression Quantitative Trait Locus rs237895 in the Intergenerational Transmission of the Effects of Maternal Childhood Maltreatment. | Toepfer P et al. | β | 2019 | β |
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| Gene expression and DNA methylation are extensively coordinated with MRI-based brain microstructural characteristics. | Gaiteri C et al. | β | 2019 | β |
| Genetic risk for Alzheimer's dementia predicts motor deficits through multi-omic systems in older adults. | Tasaki S et al. | β | 2019 | β |
| Genome-wide association analysis reveals KCTD12 and miR-383-binding genes in the background of rumination. | Eszlari N et al. | β | 2019 | β |
| Genome-wide germline correlates of the epigenetic landscape of prostate cancer. | Houlahan KE et al. | β | 2019 | β |
| Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. | Jansen IE et al. | β | 2019 | β |
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| Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer's disease. | Semick SA et al. | β | 2019 | β |
| Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes. | Zhao T et al. | β | 2019 | β |
| Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms. | Zhao S et al. | β | 2019 | β |
| Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder. | Zhong J et al. | β | 2019 | β |
| Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. | Taylor DL et al. | β | 2019 | β |
| Investigating the energy crisis in Alzheimer disease using transcriptome study. | Dharshini SAP et al. | β | 2019 | β |
| Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease. | Hatcher C et al. | β | 2019 | β |
| Low-frequency variation in TP53 has large effects on head circumference and intracranial volume. | Haworth S et al. | β | 2019 | β |
| Mapping causal pathways from genetics to neuropsychiatric disorders using genome-wide imaging genetics: Current status and future directions. | Le BD et al. | β | 2019 | β |
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| Neuropathological correlates and genetic architecture of microglial activation in elderly human brain. | Felsky D et al. | β | 2019 | β |
| Prioritizing Parkinson's disease genes using population-scale transcriptomic data. | Li YI et al. | β | 2019 | β |
| Systems biology and gene networks in Alzheimer's disease. | Wang ZT et al. | β | 2019 | β |
| The depression GWAS risk allele predicts smaller cerebellar gray matter volume and reduced SIRT1 mRNA expression in Chinese population. | Liu W et al. | β | 2019 | β |
| The endocytic membrane trafficking pathway plays a major role in the risk of Parkinson's disease. | Bandres-Ciga S et al. | β | 2019 | β |
| TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits. | Nagpal S et al. | β | 2019 | β |
| Translational bioinformatics in mental health: open access data sources and computational biomarker discovery. | Tenenbaum JD et al. | β | 2019 | β |
| Using Openly Accessible Resources to Strengthen Causal Inference in Epigenetic Epidemiology of Neurodevelopment and Mental Health. | Walton E et al. | β | 2019 | β |
| Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain. | Ng B et al. | β | 2019 | β |
| Variations and expression features of CYP2D6 contribute to schizophrenia risk | Ma L et al. | β | 2019 | β |
| Alzheimer's Disease Risk Variant rs2373115 Regulates GAB2 and NARS2 Expression in Human Brain Tissues. | Liu G et al. | β | 2018 | β |
| Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. | Girdhar K et al. | β | 2018 | β |
| Deconstructing and targeting the genomic architecture of human neurodegeneration. | De Jager PL et al. | β | 2018 | β |
| Disease status affects the association between rs4813620 and the expression of Alzheimer's disease susceptibility gene <i>TRIB3</i>. | Liu G et al. | β | 2018 | β |
| Epigenetics applied to psychiatry: Clinical opportunities and future challenges. | Kular L et al. | β | 2018 | β |
| Evaluating the role of genetic variation in the epigenome in health and disease. | Roostaei T et al. | β | 2018 | β |
| Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. | Wray NR et al. | β | 2018 | β |
| Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. | International League Against Epilepsy Consortium on Complex Epilepsies | β | 2018 | β |
| Identification of expression quantitative trait loci associated with schizophrenia and affective disorders in normal brain tissue. | Bhalala OG et al. | β | 2018 | β |
| Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. | Qi T et al. | β | 2018 | β |
| Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility. | Raj T et al. | β | 2018 | β |
| Large-scale meta-analysis highlights the hypothalamic-pituitary-gonadal axis in the genetic regulation of menstrual cycle length. | Laisk T et al. | β | 2018 | β |
| Multi-omic Directed Networks Describe Features of Gene Regulation in Aged Brains and Expand the Set of Genes Driving Cognitive Decline. | Tasaki S et al. | β | 2018 | β |
| Polygenic analysis of inflammatory disease variants and effects on microglia in the aging brain. | Felsky D et al. | β | 2018 | β |
| Religious Orders Study and Rush Memory and Aging Project. | Bennett DA et al. | β | 2018 | β |
| Replicated associations of FADS1, MAD1L1, and a rare variant at 10q26.13 with bipolar disorder in Chinese population. | Zhao L et al. | β | 2018 | β |
| The early care environment and DNA methylome variation in childhood. | Garg E et al. | β | 2018 | β |
| The Molecular and Neuropathological Consequences of Genetic Risk for Alzheimer's Dementia. | Tasaki S et al. | β | 2018 | β |
| What is the epigenome and is it involved in multiple sclerosis? | Lee JY et al. | β | 2018 | β |